Robust generalised quadratic discriminant analysis

نویسندگان

چکیده

Quadratic discriminant analysis (QDA) is a widely used statistical tool to classify observations from different multivariate Normal populations. The generalized quadratic (GQDA) classification rule/classifier, which generalizes the QDA and minimum Mahalanobis distance (MMD) classifiers discriminate between populations with underlying elliptically symmetric distributions competes quite favorably classifier when it optimal performs much better fails under non-Normal heavy tail, e.g. Cauchy distribution. However, rule in GQDA still based on sample mean vector dispersion matrix of training set, are extremely non-robust data contamination. In real world, however, common face highly vulnerable outliers so lack robustness classical estimators reduces efficiency significantly, increasing misclassification errors. present paper investigates performance therein replaced by various robust counterparts. Applications sets as well simulation studies reveal far proposed versions classifier. A comparative study has been made advocate appropriate choice be specific situation.

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ژورنال

عنوان ژورنال: Pattern Recognition

سال: 2021

ISSN: ['1873-5142', '0031-3203']

DOI: https://doi.org/10.1016/j.patcog.2021.107981